Hybrid Neural Networks of the Olfactory Learning Center in the Drosophila Brain

Abstract

Biological signal encoding is shaped by the underlying neural circuitry. In Drosophila melanogaster, the mushroom body (MB) houses thousands of Kenyon cells (KCs) that process olfactory signals from hundreds of projection neurons (PNs). Previous studies debated the connectivity between PNs and KCs (random vs. structured). Our multiscale analysis of electron microscopic data revealed a hybrid network with diverse synaptic connection preferences and input divergence across different KC classes. Using MB connectome data, our simulation model, validated via functional imaging, accurately predicted distinct chemical sensitivities in the major KC classes. The model suggests that the hybrid network excels in detecting food odors while maintaining precise odor discrimination in different KC classes. These findings underscore the computational advantages of this hybrid network.